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Journal articles on the topic 'Autonomous drones'
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1
Yulianto, Ahmad Wilda, Dhandi Yudhit Yuniar, and Yoyok Heru Prasetyo. "Navigation and Guidance for Autonomous Quadcopter Drones Using Deep Learning on Indoor Corridors." Jurnal Jartel Jurnal Jaringan Telekomunikasi 12, no. 4 (December 30, 2022): 258–64. http://dx.doi.org/10.33795/jartel.v12i4.422.
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Autonomous drones require accurate navigation and localization algorithms to carry out their duties. Outdoors drones can utilize GPS for navigation and localization systems. However, GPS is often unreliable or not available at all indoors. Therefore, in this research, an autonomous indoor drone navigation model was created using a deep learning algorithm, to assist drone navigation automatically, especially in indoor corridor areas. In this research, only the Caddx Ratel 2 FPV camera mounted on the drone was used as an input for the deep learning model to navigate the drone forward without a collision with the wall in the corridor. This research produces two deep learning models, namely, a rotational model to overcome a drone's orientation deviations with a loss of 0.0010 and a mean squared error of 0.0009, and a translation model to overcome a drone's translation deviation with a loss of 0.0140 and a mean squared error of 0.011. The implementation of the two models on autonomous drones reaches an NCR value of 0.2. The conclusion from the results obtained in this research is that the difference in resolution and FOV value in the actual image captured by the FPV camera on the drone with the image used for training the deep learning model results in a discrepancy in the output value during the implementation of the deep learning model on autonomous drones and produces low NCR implementation values.
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Suzuki, Satoshi, and Kenzo Nonami. "Special Issue on Novel Technology of Autonomous Drone." Journal of Robotics and Mechatronics 33, no. 2 (April 20, 2021): 195. http://dx.doi.org/10.20965/jrm.2021.p0195.
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In the past three years, there has been rapid progress in the use of drones in society. Drones, which were previously used only experimentally in various industrial fields, are now being used in earnest in everyday operations. Drones are becoming indispensable tools in several industrial fields, such as surveying, inspection, and agriculture. At the same time, there has also been dramatic progress in autonomous drone technology. With the advancement of image processing, simultaneous localization and mapping (SLAM), and artificial intelligence technologies, many intelligent drones that apply these technologies are being researched. At the same time, our knowledge of multi-rotor helicopters, the main type of drones, has continued to deepen. As the strengths and weaknesses of multi-rotor helicopters have gradually become clearer, drones with alternate structures, such as flapping-wing drones, have come to attract renewed attention. In addition, the range of applications for drones, including passenger drones, has expanded greatly, and research on unprecedented drone operations, as well as research on systems and controls to ensure operational safety, is actively being conducted. This special issue contains the latest review, research papers, and development reports on autonomous drones classified as follows from the abovementioned perspectives. · Research on drone airframes and structures · Research on drone navigation and recognition with a focus on image processing · Research on advanced drone controls · Research and development of drone applications We hope that the readers will actively promote the use of drones in their own research and work, based on the information obtained from this special issue.
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Kang, Tae-Won, and Jin-Woo Jung. "A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management." Drones 8, no. 4 (March 29, 2024): 130. http://dx.doi.org/10.3390/drones8040130.
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The ongoing expansion of the Fourth Industrial Revolution has led to a diversification of drone applications. Among them, this paper focuses on the critical technology required for load management using drones. Generally, when using autonomous drones, global positioning system (GPS) receivers attached to the drones are used to determine the drone’s position. However, GPS integrated into commercially available drones have an error margin on the order of several meters. This paper, proposes a method that uses fixed-size quick response (QR) codes to maintain the error of drone 3D localization within a specific range and enable accurate mapping. In the drone’s 3D localization experiment, the errors were maintained within a specific range, with average errors ranging from approximately 0 to 3 cm, showing minimal differences. During the mapping experiment, the average error between the actual and estimated positions of the QR codes was consistently around 0 to 3 cm.
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Gupta, Arpit. "Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5452–58. http://dx.doi.org/10.22214/ijraset.2021.36144.
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Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.
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Ma, Jinyu, Puhui Chen, Xinhan Xiong, Liangcheng Zhang, Shengdong Yu, and Dongyuan Zhang. "Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones." Drones 8, no. 4 (March 28, 2024): 127. http://dx.doi.org/10.3390/drones8040127.
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A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.
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Hutchinson, William. "Deceiving Autonomous Drones." International Journal of Cyber Warfare and Terrorism 10, no. 3 (July 2020): 1–14. http://dx.doi.org/10.4018/ijcwt.2020070101.
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This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.
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Montes-Romero, Ángel, Arturo Torres-González, Jesús Capitán, Maurizio Montagnuolo, Sabino Metta, Fulvio Negro, Alberto Messina, and Aníbal Ollero. "Director Tools for Autonomous Media Production with a Team of Drones." Applied Sciences 10, no. 4 (February 21, 2020): 1494. http://dx.doi.org/10.3390/app10041494.
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This paper proposes a set of director tools for autonomous media production with a team of drones. There is a clear trend toward using drones for media production, and the director is the person in charge of the whole system from a production perspective. Many applications, mainly outdoors, can benefit from the use of multiple drones to achieve multi-view or concurrent shots. However, there is a burden associated with managing all aspects in the system, such as ensuring safety, accounting for drone battery levels, navigating drones, etc. Even though there exist methods for autonomous mission planning with teams of drones, a media director is not necessarily familiar with them and their language. We contribute to close this gap between media crew and autonomous multi-drone systems, allowing the director to focus on the artistic part. In particular, we propose a novel language for cinematography mission description and a procedure to translate those missions into plans that can be executed by autonomous drones. We also present our director’s Dashboard, a graphical tool allowing the director to describe missions for media production easily. Our tools have been integrated into a real team of drones for media production and we show results of example missions.
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Gugan, Gopi, and Anwar Haque. "Path Planning for Autonomous Drones: Challenges and Future Directions." Drones 7, no. 3 (February 28, 2023): 169. http://dx.doi.org/10.3390/drones7030169.
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Unmanned aerial vehicles (UAV), or drones, have gained a lot of popularity over the last decade. The use of autonomous drones appears to be a viable and low-cost solution to problems in many applications. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible and energy-efficient paths to avoid collisions. In this study, we review two key aspects of path planning: environmental representation and path generation techniques. Common path planning techniques are analyzed, and their key limitations are highlighted. Finally, we review thirty-five highly cited publications to identify current trends in drone path planning research. We then use these results to identify factors that need to be addressed in future studies in order to develop a practical path planner for autonomous drones.
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Pikalov, Simon, Elisha Azaria, Shaya Sonnenberg, Boaz Ben-Moshe, and Amos Azaria. "Vision-Less Sensing for Autonomous Micro-Drones." Sensors 21, no. 16 (August 5, 2021): 5293. http://dx.doi.org/10.3390/s21165293.
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This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness.
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Rodriguez, Angel A., Mohammad Shekaramiz, and Mohammad A. S. Masoum. "Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation." Drones 8, no. 4 (April 17, 2024): 154. http://dx.doi.org/10.3390/drones8040154.
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The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems.
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Gupta, Myra. "Reinforcement Learning for Autonomous Drone Navigation." Innovative Research Thoughts 9, no. 5 (2023): 11–20. http://dx.doi.org/10.36676/irt.2023-v9i5-002.
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Drone navigation involves the process of controlling the movement and flight path of unmanned aerial vehicles (UAVs). It encompasses both the hardware and software systems that enable drones to navigate and maneuver autonomously or under the guidance of a human operator. The utility of drone navigation is vast and varied, making it a critical component in numerous industries and applications. Firstly, drone navigation plays a crucial role in aerial surveillance and reconnaissance. Drones equipped with advanced navigation systems can efficiently patrol large areas, monitor activities, and gather real-time data from various perspectives. This capability is particularly valuable in security and law enforcement operations, disaster response, and environmental monitoring, where access and visibility might be limited.
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Edelman, Harry, Joel Stenroos, Jorge Peña Queralta, David Hästbacka, Jani Oksanen, Tomi Westerlund, and Juha Röning. "Analysis of airport design for introducing infrastructure for autonomous drones." Facilities 41, no. 15/16 (July 21, 2023): 85–100. http://dx.doi.org/10.1108/f-11-2022-0146.
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Purpose Connecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data. Design/methodology/approach The paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones. Findings The key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle. Research limitations/implications As the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research. Practical implications The findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment. Social implications The evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability. Originality/value To the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.
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Gajana, Kanade Dnyaneshwar. "Medical Supplies Delivery Autonomous Drone with Security." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 6022–30. http://dx.doi.org/10.22214/ijraset.2024.61335.
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Abstract: Drones and UAVs have gained a lot of attention in the recent years. Being fast and efficient, they have the capability to carry out a multitude of tasks efficiently and quickly. One of the critical applications of drones is in the field of healthcare is the delivery of medical supplies. In case of severe natural calamities, only a drone is suitable for providing the basic essential supplies that can save the lives of constrained survivors. In rural and underdeveloped areas, a drone is ideal for delivery. The usage of a medical drone is not restricted as it can easily be used in day-to-day urban and sub-urban areas. It is an efficient method to deliver medicines and aid to patients and those in need.
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Triditya, Gregory, Mgs M. Luthfi Ramadhan, and Wisnu Jatmiko. "Enhancing Assault Maneuvers in Simulated Scenarios of Multiple Invader Kamikaze Drones through the Utilization of a Modified Adaptive Elforce Algorithm." Jurnal Ilmu Komputer dan Informasi 17, no. 1 (February 25, 2024): 67–75. http://dx.doi.org/10.21609/jiki.v17i1.1202.
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The development of autonomous drone technology has led in their widespread deployment, especially in combat scenarios. One instance of this is the utilization of kamikaze drones, as seen in the Ukraine war. Autonomous defense drones have been used to counter these invading kamikaze drones. This study focuses on simulating scenarios involving invader vs. defender drones, primarily exploring invader drone maneuver motions to maximize damage inflicted on chosen targets. The work we conducted presents an enhanced el-force algorithm that employs Coulomb's Law-based maneuver techniques to improve the effectiveness of multiple kamikaze invader drones when engaging target defended by defender drones. We aim to improve traditional el-force by addressing key challenges such as siege tendencies and unproductive conduct. In addition, we explore various attacking formations to determine the most effective formation. To evaluate the performance of our proposed algorithm, we conducted simulation in a dynamic 3D environment, employing damage inflicted as the evaluation metric. Through rigorous testing, we conclusively demonstrate that our proposed method combining with a circular formation, outperforms alternative attacking maneuvers and formations. Our findings provide insights into optimal maneuver movements and attacking formations, improving the effectiveness of invader drones in engaging and damaging designated targets.
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Haque, Ahshanul, and Md Naseef-Ur-Rahman Chowdhury. "Exploring the Benefits of Reinforcement Learning for Autonomous Drone Navigation and Control." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5808–14. http://dx.doi.org/10.35444/ijana.2023.15110.
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Drones are now used in a wide range of industries, including delivery services and agriculture. Notwithstanding, controlling robots in powerful conditions can be testing, particularly while performing complex assignments. Conventional strategies for drone mechanization depend on pre-customized directions, restricting their adaptability and versatility. Drones can learn from their interactions with their environment and improve their performance over time with the help of reinforcement learning (RL), which has emerged as a promising method for drone automation in recent years. This paper looks at how RL can be used to automate drones and how it can be used in different industries. In addition, the difficulties of RL-based drone automation and potential directions for future research are discussed in the paper.
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González, Roberto J. "Death by remote control: Drone warfare in Afghanistan, Ukraine and beyond." Anthropology Today 40, no. 1 (January 31, 2024): 7–11. http://dx.doi.org/10.1111/1467-8322.12862.
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This article examines the use of military drones over the past 20 years. It includes the following: (1) a critical review of how drone warfare in the US‐led ‘war on terror’ affected drone crews and those ‘living under drones’; (2) an analysis of how drone warfare in Ukraine and Russia differs from previous deployments of the technology; and (3) a careful assessment of what lies ahead, as national governments and technology firms race to develop AI‐enabled drones and autonomous weapons systems on a broad scale.
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Rojas-Perez, Leticia Oyuki, and Jose Martinez-Carranza. "DeepPilot: A CNN for Autonomous Drone Racing." Sensors 20, no. 16 (August 13, 2020): 4524. http://dx.doi.org/10.3390/s20164524.
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Autonomous Drone Racing (ADR) was first proposed in IROS 2016. It called for the development of an autonomous drone capable of beating a human in a drone race. After almost five years, several teams have proposed different solutions with a common pipeline: gate detection; drone localization; and stable flight control. Recently, Deep Learning (DL) has been used for gate detection and localization of the drone regarding the gate. However, recent competitions such as the Game of Drones, held at NeurIPS 2019, called for solutions where DL played a more significant role. Motivated by the latter, in this work, we propose a CNN approach called DeepPilot that takes camera images as input and predicts flight commands as output. These flight commands represent: the angular position of the drone’s body frame in the roll and pitch angles, thus producing translation motion in those angles; rotational speed in the yaw angle; and vertical speed referred as altitude h. Values for these 4 flight commands, predicted by DeepPilot, are passed to the drone’s inner controller, thus enabling the drone to navigate autonomously through the gates in the racetrack. For this, we assume that the next gate becomes visible immediately after the current gate has been crossed. We present evaluations in simulated racetrack environments where DeepPilot is run several times successfully to prove repeatability. In average, DeepPilot runs at 25 frames per second (fps). We also present a thorough evaluation of what we called a temporal approach, which consists of creating a mosaic image, with consecutive camera frames, that is passed as input to the DeepPilot. We argue that this helps to learn the drone’s motion trend regarding the gate, thus acting as a local memory that leverages the prediction of the flight commands. Our results indicate that this purely DL-based artificial pilot is feasible to be used for the ADR challenge.
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Anderson, Kara. "Autonomous Archaeological Authority: The Future of Drone Use and Privacy Laws in Cultural Heritage Preservation." Journal of Air Law and Commerce 88, no. 3 (2023): 635. http://dx.doi.org/10.25172/jalc.88.3.4.
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Since ancient times, humanity has placed high value on natural and cultural phenomena, with Philo of Byzantium recording the first list of the “Seven Wonders of the Ancient World” as early as 225 B.C.E. Similarly, modern world leaders continue to recognize the value of these and more sites through preserving them as United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage sites. With the advancement of drone technology, researchers now employ drones to aid preservation efforts since drones can enter dangerous and humanly-inaccessible spaces, provide detailed images of sites the human eye cannot see, and assist governments in identifying illegal looting. However, while many countries have developed drone use regulations, the challenging ethical questions drones pose regarding privacy rights have resulted in a lack of drone-specific privacy regulations. As countries create new legislation to fill the policy gaps, the tension between protecting privacy rights and preserving cultural heritage results in an unclear future for the use of drones for site preservation. Section II of this Comment analyzes the history of World Heritage sites, drone development, and their intersection to understand the vital role drones play in site preservation. Subsequently, Section III conducts a comparative analysis of drone-use and privacy regulations in four countries with the greatest amount of UNESCO sites to identify the current status of global drone laws. Finally, Section IV addresses the lack of drone-specific privacy regulation and asserts potential implications new drone legislation could have on preservation efforts while postulating methods to protect preservation drone use.
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Pillai, Gokulnath M., Ajith Suresh, Eikansh Gupta, Vinod Ganapathy, and Arpita Patra. "Privadome: Delivery Drones and Citizen Privacy." Proceedings on Privacy Enhancing Technologies 2024, no. 2 (April 2024): 29–48. http://dx.doi.org/10.56553/popets-2024-0039.
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E-commerce companies are actively considering the use of delivery drones for customer fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semi-autonomous or fully-autonomous drones. Footage of ground-based citizens captured in these videos may lead to privacy concerns. This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS. PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen’s location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage.
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Aravind, Rajeswari, and S. Mathivathani. "Overview of Quad Copter and Its Utilitarian." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 503–6. http://dx.doi.org/10.1166/jctn.2019.7758.
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The focus of this research is drone which is called as the UAV (unmanned aerial vehicle). They include both autonomous drones and remotely piloted vehicles (RPVs). The article highlights on feasibility of drone in various applications and its variable mechanism. Features of drones are also discussed here. Drones are not to given licenses by several governments since they pose a threat to privacy and also security. Therefore an active surveillance scheme has to be developed to monitor its uncontrolled use.
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De Wagter, Christophe, Federico Paredes-Vallé, Nilay Sheth, and Guido de Croon. "The sensing, state-estimation, and control behind the winning entry to the 2019 Artificial Intelligence Robotic Racing Competition." Field Robotics 2, no. 1 (March 10, 2022): 1263–90. http://dx.doi.org/10.55417/fr.2022042.
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Autonomous drone racing currently forms an extreme challenge in robotics. While human drone racers can fly through complex tracks at speeds of up to 190 km/h (53 m/s), autonomous drones still need to tackle several fundamental problems in AI under severe restrictions in terms of resources before they reach the same adaptability and speed. In this article, we present the winning solution of the first AI Robotic Racing (AIRR) Circuit, an autonomous drone race competition in which all participating teams used the same drone, to which they had limited access. The core of our approach is inspired by how human pilots combine noisy observations of the race gates with a mental model of the drone’s dynamics. The navigation is based on gate detection with an efficient deep neural segmentation network and active vision. Combined with contributions to robust state estimation and risk-based control, our solution was able to reach speeds of ≈33 km/h (9.2m/s) and hereby more than triple the speeds seen in previous autonomous drone race competitions. This work analyses the performance of each component and discusses the implications for high-performance real-world AI applications with limited training time.
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Muñoz-Gómez, Antonio-Miguel, Juan-Manuel Marredo-Píriz, Javier Ballestín-Fuertes, and José-Francisco Sanz-Osorio. "A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones." Applied Sciences 13, no. 18 (September 10, 2023): 10175. http://dx.doi.org/10.3390/app131810175.
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Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.
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Kashkarov, Anton, Volodymyr Diordiiev, Andrii Sabo, and Gennadii Novikov. "Semi-Autonomous Drone for Agriculture on the Tractor Base." Acta Technologica Agriculturae 21, no. 4 (December 1, 2018): 149–52. http://dx.doi.org/10.2478/ata-2018-0027.
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Abstract This paper deals with the prospects of using a drone for spraying the gardens and vineyards. Relevance of this process is substantiated with the help of statistical data on the industry in Ukraine. To increase the efficiency of drones during the plant treatment, the concept of a semi-autonomous drone is proposed with connection to a communication line with a tractor – a “tractor-drone” complex. A spraying solution and commands for the drone are transmitted via the communication line. Basic physical formulas for appropriate selection of technical means for the lifting of sprayer frame are presented. Environmental parameters for the flight control system were estimated: temperature fluctuation at 20 K requires screw speed increase by 1.5%; an increase in atmospheric pressure by 5% allows reduction of screw speed by 2%. Tasks of the control system for the concept of semi-autonomous drones are defined in the paper.
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Lee, Alvin, Suet-Peng Yong, Witold Pedrycz, and Junzo Watada. "Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment." Algorithms 17, no. 4 (March 27, 2024): 139. http://dx.doi.org/10.3390/a17040139.
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Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view.
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Luo, Yuan, Jiakai Lu, Yi Zhang, Qiong Qin, and Yanyu Liu. "3D JPS Path Optimization Algorithm and Dynamic-Obstacle Avoidance Design Based on Near-Ground Search Drone." Applied Sciences 12, no. 14 (July 21, 2022): 7333. http://dx.doi.org/10.3390/app12147333.
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As various fields and industries have progressed, the use of drones has grown tremendously. The problem of path planning for drones flying at low altitude in urban as well as mountainous areas will be crucial for drones performing search-and-rescue missions. In this paper, we propose a convergent approach to ensure autonomous collision-free path planning for drones in the presence of both static obstacles and dynamic threats. Firstly, this paper extends the jump point search algorithm (JPS) in three dimensions for the drone to generate collision-free paths based on static environments. Next, a parent node transfer law is proposed and used to implement the JPS algorithm for any-angle path planning, which further shortens the planning path of the drones. Furthermore, the optimized paths are smoothed by seventh-order polynomial interpolation based on minimum snap to ensure the continuity at the path nodes. Finally, this paper improves the artificial potential field (APF) method by a virtual gravitational field and 3D Bresenham’s line algorithm to achieve the autonomous obstacle avoidance of drones in a dynamic-threat conflict environment. In this paper, the performance of this convergent approach is verified by simulation experiments. The simulation results show that the proposed approach can effectively solve the path planning and autonomous-obstacle-avoidance problems of drones in low-altitude flight missions.
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Chronis, Christos, Georgios Anagnostopoulos, Elena Politi, Antonios Garyfallou, Iraklis Varlamis, and George Dimitrakopoulos. "Path planning of autonomous UAVs using reinforcement learning." Journal of Physics: Conference Series 2526, no. 1 (June 1, 2023): 012088. http://dx.doi.org/10.1088/1742-6596/2526/1/012088.
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Abstract Autonomous BVLOS Unmanned Aerial Vehicles (UAVs) are gradually gaining their share in the drone market. Together with the demand for extended levels of autonomy comes the necessity for high-performance obstacle avoidance and navigation algorithms that will allow autonomous drones to operate with minimum or no human intervention. Traditional AI algorithms have been extensively used in the literature for finding the shortest path in 2-D or 3-D environments and navigating the drones successfully through a known and stable environment. However, the situation can become much more complicated when the environment is changing or not known in advance. In this work, we explore the use of advanced artificial intelligence techniques, such as reinforcement learning, to successfully navigate a drone within unspecified environments. We compare our approach against traditional AI algoriths in a set of validation experiments on a simulation environment, and the results show that using only a couple of low-cost distance sensors it is possible to successfully navigate the drone beyond the obstacles.
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Langåker, Helge-André, Håkon Kjerkreit, Christoffer L. Syversen, Richard JD Moore, Øystein H. Holhjem, Irene Jensen, Aiden Morrison, et al. "An autonomous drone-based system for inspection of electrical substations." International Journal of Advanced Robotic Systems 18, no. 2 (March 1, 2021): 172988142110029. http://dx.doi.org/10.1177/17298814211002973.
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In the years to come, large power grid operators will operate and maintain an ever-increasing asset base. New innovative solutions are needed to increase the quality and efficiency of asset management to avoid corresponding growth in resources and cost. To this end, autonomous unmanned aerial vehicles (UAVs) provide a range of possibilities. Here, we present a novel prototype solution for autonomous and remotely operated inspection missions with resident drones on electrical substations, comprising: (1) an autonomous drone with sense and avoid and robustness to harsh weather capability; (2) a drone hangar for remote operations; and (3) drone operations and data acquisition management software. Further, we discuss the possibilities and challenges that such a system offers and give an overview of requirements that are key to realizing the potential of drones for improved asset management. These requirements are based on years of operational experience with electrical substations combined with the lessons learned during the development and testing of our drone system. We also experimentally investigate safety distances between the drone and high-voltage infrastructure. We demonstrate the usefulness of our autonomous inspection solution through extensive field testing at one of Statnett’s fully operational electrical substations.
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Ahmad, Hijaz, Muhammad Farhan, and Umar Farooq. "Computer Vision Techniques for Military Surveillance Drones." Wasit Journal of Computer and Mathematics Science 2, no. 2 (July 1, 2023): 56–63. http://dx.doi.org/10.31185/wjcms.148.
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Commercial unmanned aerial vehicles (UAVs), also referred to as drones, have proliferated recently, raising concerns about security threats and the need for effective countermeasures. To address these concerns, various technologies have been explored, including radar, acoustics, and RF signal analysis. However, computer vision, particularly deep learning approaches, has emerged as a robust and widely used method for autonomous drone identification. The goal of this research is to create an autonomous drone identification and surveillance system that makes use of a mix of static wide-angle cameras and a lower-angle camera placed on a revolving turret. To optimize memory and processing time, we suggested a novel multi-frame DL identification model. In this approach, the frames captured by the turret's magnified camera are stacked on top of the frames from the wide-angle still camera. Utilizing this technique, we can create an efficient pipeline that conducts initial identification of small-sized aerial invaders on the primary picture plane and identification on the expanded image plane at the same time. This approach significantly reduces the computational burden associated with detection algorithms, making it more resource-efficient. Furthermore, we present the complete system architecture, which includes DL classification frameworks, tracking algorithms, and other essential components. By integrating these elements, we create a comprehensive solution for drone identification and tracking. The system leverages the power of deep learning to accurately classify and track drones in real-time, enabling prompt response and mitigating potential security threats. Overall, this research offers a novel and effective approach to autonomously identify and track drones using computer vision and deep learning techniques. By combining static and dynamic camera perspectives and employing a multi-frame detection method, we provide a resource-efficient solution for drone identification. This work contributes to the ongoing efforts in enhancing security measures against potential drone-related risks
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Ngoua Ndong Avele, J. B., and V. S. Goryainov. "UAV Docking Station: Study on Building an Autonomous Takeoff and Landing Platform for Unmanned Aerial Vehicles." LETI Transactions on Electrical Engineering & Computer Science 16, no. 9 (2023): 38–48. http://dx.doi.org/10.32603/2071-8985-2023-16-9-38-48.
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Studies how to increase the efficiency of autonomous operation of drones using an intelligent docking station for unmanned aerial vehicles (UAVs), which improves charging and maintenance, reducing the need for human intervention in these processes. Known examples of designs for an automatic drone recharging system have been considered. The results present a system developed for automatic landing of drones on the platform and concepts for systems for automatically positioning the drone after landing and for wireless charging or replacing the drone battery.
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Ashush, Nerya, Shlomo Greenberg, Erez Manor, and Yehuda Ben-Shimol. "Unsupervised Drones Swarm Characterization Using RF Signals Analysis and Machine Learning Methods." Sensors 23, no. 3 (February 1, 2023): 1589. http://dx.doi.org/10.3390/s23031589.
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Autonomous unmanned aerial vehicles (UAVs) have attracted increasing academic and industrial attention during the last decade. Using drones have broad benefits in diverse areas, such as civil and military applications, aerial photography and videography, mapping and surveying, agriculture, and disaster management. However, the recent development and innovation in the field of drone (UAV) technology have led to malicious usage of the technology, including the penetration of secure areas (such as airports) and serving terrorist attacks. Autonomous weapon systems might use drone swarms to perform more complex military tasks. Utilizing a large number of drones, simultaneously increases the risk and the reliability of the mission in terms of redundancy, survivability, scalability, and the quality of autonomous performance in a complex environment. This research suggests a new approach for drone swarm characterization and detection using RF signals analysis and various machine learning methods. While most of the existing drone detection and classification methods are typically related to a single drone classification, using supervised approaches, this research work proposes an unsupervised approach for drone swarm characterization. The proposed method utilizes the different radio frequency (RF) signatures of the drone’s transmitters. Various kinds of frequency transform, such as the continuous, discrete, and wavelet scattering transform, have been applied to extract RF features from the radio frequency fingerprint, which have then been used as input for the unsupervised classifier. To reduce the input data dimension, we suggest using unsupervised approaches such as Principal component analysis (PCA), independent component analysis (ICA), uniform manifold approximation and projection (UMAP), and the t-distributed symmetric neighbor embedding (t-SNE) algorithms. The proposed clustering approach is based on common unsupervised methods, including K-means, mean shift, and X-means algorithms. The proposed approach has been evaluated using self-built and common drone swarm datasets. The results demonstrate a classification accuracy of about 95% under additive Gaussian white noise with different levels of SNR.
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Abdallah, Shalaw M. "Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller." Cihan University-Erbil Scientific Journal 7, no. 2 (December 20, 2023): 54–59. http://dx.doi.org/10.24086/cuesj.v7n2y2023.pp54-59.
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Drones have been frequently used for photography in recent years at significantly cheaper rates. However, the most modern drones are exceedingly error-prone and require precise manual control to take high-quality photos or films. We suggest using the AI method of Haar cascades with a PID controller to give drones vision, enabling them to do autonomous tracking and detection. This project aims to improve photography fields. The proposed system tries to detect the face and track the person's movements. This system will help photographers and journalists upgrade their work, even if it is used in surveillance and the military. The algorithm's results show that the DJI Tello tiny drone's camera is capable of detecting and tracking faces. The micro drone was picked since it is lightweight and compact, making its use safe and enabling testing to take place inside. Additionally, the DJI Tello may be easily programmed using Python. The position of the drone is contrasted with the set point in the center of the image to identify errors, allowing control signals for calculating forward/backwards, right/left, and yaw movements. The proposed system results show that the drone can detect and track the face very well, and the PID values are stable.
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Amam Hossain Bagdadee, Et al. "A Novel Method for Self-Driving Solar-Powered Drones." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (February 13, 2024): 4727–41. http://dx.doi.org/10.17762/ijritcc.v11i9.10024.
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This project presented the transformative potential of integrating solar panels into drones. This innovative approach addresses the long-standing issue of limited battery life, enabling drones to operate continuously, adapt to changing mission demands, and contribute to sustainability efforts in the field of unmanned aerial vehicles. This development represents a significant step forward in the evolution of drone technology, promising a more versatile and self-sustaining future for drones across various sectors. Factors like high-speed flight, aggressive maneuvers, heavy payloads, and adverse weather can dramatically reduce battery life. Surveillance drones, for instance, are confined to covering limited areas before returning for battery changes or recharging. A groundbreaking solution lies in the incorporation of solar panels directly into the drones, allowing them to self-charge when required. This innovation ensures uninterrupted drone operation, regardless of the prospects of energy demands, thus marking a significant step forward in drone technology. With this integration of solar power, drones are poised to become not only versatile but also autonomous, promising a transformative development in the world of unmanned aerial vehicles.
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Marino, Francesco, and Giorgio Guglieri. "Beyond Static Obstacles: Integrating Kalman Filter with Reinforcement Learning for Drone Navigation." Aerospace 11, no. 5 (May 15, 2024): 395. http://dx.doi.org/10.3390/aerospace11050395.
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Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone’s real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings.
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Lynskey, Jared, Kyi Thar, Thant Oo, and Choong Hong. "Facility Location Problem Approach for Distributed Drones." Symmetry 11, no. 1 (January 20, 2019): 118. http://dx.doi.org/10.3390/sym11010118.
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Currently, industry and academia are undergoing an evolution in developing the next generation of drone applications. Including the development of autonomous drones that can carry out tasks without the assistance of a human operator. In spite of this, there are still problems left unanswered related to the placement of drone take-off, landing and charging areas. Future policies by governments and aviation agencies are inevitably going to restrict the operational area where drones can take-off and land. Hence, there is a need to develop a system to manage landing and take-off areas for drones. Additionally, we proposed this approach due to the lack of justification for the initial location of drones in current research. Therefore, to provide a foundation for future research, we give a justified reason that allows predetermined location of drones with the use of drone ports. Furthermore, we propose an algorithm to optimally place these drone ports to minimize the average distance drones must travel based on a set of potential drone port locations and tasks generated in a given area. Our approach is derived from the Facility Location problem which produces an efficient near optimal solution to place drone ports that reduces the overall drone energy consumption. Secondly, we apply various traveling salesman algorithms to determine the shortest route the drone must travel to visit all the tasks.
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Rao, Gurrala Madhusudhana, B. Lakshmi Prasanna, Katuri Rayudu, Vempalle Yeddula Kondaiah, Boyanasetti Venkata Sai Thrinath, and Talla Venu Gopal. "Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 2 (June 1, 2024): 733. http://dx.doi.org/10.11591/ijpeds.v15.i2.pp733-743.
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The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.
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P Kalidas, Amudhini, Christy Jackson Joshua, Abdul Quadir Md, Shakila Basheer, Senthilkumar Mohan, and Sapiah Sakri. "Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles." Drones 7, no. 4 (April 1, 2023): 245. http://dx.doi.org/10.3390/drones7040245.
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Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.
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Sarvi, Batoul. "Multimedia communications for autonomous drones." Boolean 2022 VI, no. 1 (December 6, 2022): 52–58. http://dx.doi.org/10.33178/boolean.2022.1.9.
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In recent years, there has been significant growth in multimedia communication on drones. The first thing that comes to every researcher’s mind is what requirements are for multimedia communication to be acceptable for existing scenarios on UAVs? Because of the noisy wireless channel and long distance between UAVs, providing reliable and real-time multimedia communications on UAVs stands at the top of the requirements list. To the best of our knowledge, mobile edge computing and cross-layer error control have significant possibilities to provide a better quality of multimedia communication on UAVs. Finally, utilizing the aforementioned edge network techniques can increase the efficiency of the overall system, enhance the video quality, maximize the usage of network resources, and save energy in multimedia communication on UAV networks.
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Xue, Zhihan, and Tad Gonsalves. "Vision Based Drone Obstacle Avoidance by Deep Reinforcement Learning." AI 2, no. 3 (August 19, 2021): 366–80. http://dx.doi.org/10.3390/ai2030023.
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Research on autonomous obstacle avoidance of drones has recently received widespread attention from researchers. Among them, an increasing number of researchers are using machine learning to train drones. These studies typically adopt supervised learning or reinforcement learning to train the networks. Supervised learning has a disadvantage in that it takes a significant amount of time to build the datasets, because it is difficult to cover the complex and changeable drone flight environment in a single dataset. Reinforcement learning can overcome this problem by using drones to learn data in the environment. However, the current research results based on reinforcement learning are mainly focused on discrete action spaces. In this way, the movement of drones lacks precision and has somewhat unnatural flying behavior. This study aims to use the soft-actor-critic algorithm to train a drone to perform autonomous obstacle avoidance in continuous action space using only the image data. The algorithm is trained and tested in a simulation environment built by Airsim. The results show that our algorithm enables the UAV to avoid obstacles in the training environment only by inputting the depth map. Moreover, it also has a higher obstacle avoidance rate in the reconfigured environment without retraining.
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V.J, Rehna, and Mohammad Nizamuddin Inamdar. "Impact of Autonomous Drone Pollination in Date Palms." International Journal of Innovative Research and Scientific Studies 5, no. 4 (October 12, 2022): 297–305. http://dx.doi.org/10.53894/ijirss.v5i4.732.
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Artificial pollination of date palms has been practiced over thousands of years to improve the fruiting traits in date palms. Due to the changes in agricultural practices in the modern period, mechanical pollination techniques were tried in some parts of the world. But machine pollination of date palms has not gained popularity worldwide owing to economic, environmental, or technical challenges. Of late, agricultural drones were introduced to pollinate date palms in significantly less time and reduce the risk of injury, manpower, and cost. Modern drones can have integrated, built-in smart data-collecting devices to provide the farmers with all relevant information. Although this autonomous method provides a number of benefits in terms of labor and cost, pollination time, ease of use, etc, studies have not yet entirely evaluated the efficacy of drone pollination on date palms. This paper summarizes the outcomes of an autonomous drone pollination study performed during the 2022 season in the orchards of Oman. The pros and cons of this artificial aerial pollination method are examined in the paper. The impact of this method of pollination on crop yield, fruit quality, and fruit set percentage are analyzed. This study also explores the limitations of the autonomous drone pollination system and throws light on ways to improve its efficiency.
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Sondors, Marks, Ilmārs Apeināns, and Sergejs Kodors. "AUTONOMOUS UNMANNED DRONES FLIGHT PLANNING, USING A MODIFIED SHORTEST PATH ALGORITHM WITH A LIMITED TIME FRAME." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 27 (October 30, 2023): 14–18. http://dx.doi.org/10.17770/het2023.27.7375.
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The aim of this work is to develop an algorithm to find the shortest path for drone flight planning with a limited time frame. Author used the local search shortest path algorithm to find the most efficient algorithm to use for further modification to apply to a drones flight calculation. The algorithm was modified to use the distance between points as a unit of time to limit the flight path length depending on the drone's maximum flight time. As a result of the work, an algorithm was created which, upon receiving an array of points, finds the shortest distance between the points, but when it reaches the limit of the flight duration, it returns to the drone station to charge, and resumes flight once it’s done charging.
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Oh, Jooyoung. "The Kestrel Drone." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 2 (August 12, 2023): 1–10. http://dx.doi.org/10.1145/3597629.
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Artificial intelligence (AI) is redefining roads and skyways with advanced mobility technologies such as autonomous driving and Urban Air Mobility (UAM), suggesting a new paradigm for human mobility. This project sheds new light on drone mobility technology in terms of environmental aesthetics, focusing on global ecological issues. The Kestrel Drone is proposed based on a speculative scenario where "birds" become front-line users of drone technology and share their air routes with drones. At the exhibition hall, five Kestrel Drones were equipped with bird-mimicking wings and an AI tracker. The drones simulated a guiding bird for resident birds in a city. This project aims to evolve AI into a technology that has various parallel values that can coexist with non-human values, thus expanding the possibilities of technology.
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Rathod, Pornima D., and Gopal U. Shinde. "Autonomous Aerial System (UAV) for Sustainable Agriculture: A Review." International Journal of Environment and Climate Change 13, no. 8 (June 10, 2023): 1343–55. http://dx.doi.org/10.9734/ijecc/2023/v13i82080.
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Drones are now a days emerging as a component of precision agriculture along with contributing to sustainable agriculture. The use of advanced technologies such as drone in agriculture offer potential for facing several major or minor challenges. The major applications of drone in agriculture are spraying, irrigation, crop monitoring, soil and field analysis and bird control. The objective of this paper is to review the latest trends and applications of leading technologies related to agricultural UAVs equipment, and sensors development. And also, the use of UAVs in real agricultural environments. Based on the literature, found that a lots of agriculture applications can be done by using Drone. In the methodology, we used a comprehensive review from other researches in this world. Furthermore, the future development of agricultural UAVs and their challenges are considered. In this review paper, summarizes the available agricultural drones and applications of UAVs for Precision Agriculture using different sensors to evaluated agricultural parameters such as NDVI, vegetation index, NIR, nutrient disorder using sensors like RGB, digital camera, multispectral and hyperspectral sensors and to reduce the wasting of water and chemicals quadcopter, hexacopter UAVs could be used.
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Li, Kai, Ning Lu, Jingjing Zheng, Pei Zhang, Wei Ni, and Eduardo Tovar. "BloothAir." ACM Transactions on Cyber-Physical Systems 5, no. 3 (July 2021): 1–22. http://dx.doi.org/10.1145/3448254.
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Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.
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Chandraiah, G., M. Swarna Latha, K. Hema, H. Kaavya, K. Vijay Simha Reddy, and J. Dilip Babu. "Implementation of Autonomous Drone for Flood Surveillance." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 2378–84. http://dx.doi.org/10.22214/ijraset.2024.62121.
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Abstract: Floods are natural disasters that pose significant challenges to both the safety of communities and the effectiveness of response efforts. In recent years, the integration of unmanned aerial vehicles, commonly known as drones, has emerged as a valuable tool in flood surveillance. This paper provides an overview of the multifaceted role of drones in flood-related scenarios, highlighting their capabilities, benefits, and potential challenges. Drones have demonstrated their versatility in flood management by offering several critical functions. Drones enhance the safety of response teams by providing situational awareness without exposing personnel to hazardous conditions. They can be deployed to access remote or inaccessible areas, where human intervention is challenging or dangerous. Drones equipped with thermal imaging cameras also support search and rescue missions, increasing the chances of locating and saving individuals trapped by floodwaters. In addition to assessment and rescue, drones contribute to flood forecasting and early warning systems. By continuously monitoring water levels, weather conditions, and flood dynamics, drones provide essential data for improving flood prediction models. This information helps authorities issue timely warnings, enabling communities to prepare and evacuate when necessary.
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Schulzke, Marcus. "Drone Proliferation and the Challenge of Regulating Dual-Use Technologies." International Studies Review 21, no. 3 (May 18, 2018): 497–517. http://dx.doi.org/10.1093/isr/viy047.
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AbstractThe controversy surrounding military drones has generated many proposals for restricting or prohibiting existing drones, additional autonomous variants that may be created in the future, and the sale of drones to certain markets. Moreover, there is broad interest in regulating military drones, with proposals coming not only from academics but also from NGOs and policymakers. I argue that these proposals generally fail to consider the dual-use character of drones and that they therefore provide inadequate regulatory guidance. Drones are not confined to the military but rather spread across international and domestic security roles, humanitarian relief efforts, and dozens of civilian applications. Drones, their component technologies, the control infrastructure, and the relevant technical expertise would continue to develop under a military-focused regulatory regime as civilian technologies that have the potential to be militarized. I evaluate the prospects of drone regulation with the help of research on other dual-use technologies, while also showing what the study of drones can contribute to that literature. Drones’ ubiquity in nonmilitary roles presents special regulatory challenges beyond those associated with WMDs and missiles, which indicates that strict regulatory controls or international governance frameworks are unlikely to succeed. With this in mind, I further argue that future research should acknowledge that drone proliferation across military and civilian spheres is unavoidable and shift focus to considering how drone warfare may be moderated by countermeasures and institutional pressures.
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Bezas, Konstantinos, Georgios Tsoumanis, Constantinos T. Angelis, and Konstantinos Oikonomou. "Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms." Sensors 22, no. 19 (October 5, 2022): 7551. http://dx.doi.org/10.3390/s22197551.
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Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.
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Sanchez-Rodriguez, Jose-Pablo, and Alejandro Aceves-Lopez. "A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs." Robotica 36, no. 8 (May 6, 2018): 1225–43. http://dx.doi.org/10.1017/s0263574718000358.
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SUMMARYThis paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.
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Nonami, Kenzo. "Drone Technology, Cutting-Edge Drone Business, and Future Prospects." Journal of Robotics and Mechatronics 28, no. 3 (June 17, 2016): 262–72. http://dx.doi.org/10.20965/jrm.2016.p0262.
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[abstFig src='/00280003/01.jpg' width=""300"" text='Autonomous drone: MIni-surveyor MS-06LA' ] The year 2015 marked the beginning of a new phase in the history of drones in which drones came into use for business purposes in addition to the conventional ones flown for recreational purposes, and the amended Civil Aeronautic Act became effective in the same year. In 2016, full-scale drone businesses including the inspection of infrastructures, measurements, security, and disaster responses are expected to begin. An overview of the rapidly expanding drone business and the attempt to deliver objects using drones in Chiba, which is called the industrial revolution in the sky, are presented in this paper. Next, the technological characteristics of mini-surveyors using domestic, production-model drones are introduced, and the use of mini-surveyors is outlined. Cutting-edge research and overseas trends are also discussed. In addition, the approach of the mini-surveyor consortium and the amended Civil Aeronautic Act are introduced. Finally, the tough robotics challenge of an innovative research and development promotion program ImPACT involving the entire Japan and the future society where drones are spreading are described.
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Soni, Akash, and Dr M. N. Nachappa. "Cinematography Drone with Automated Ability for Self-flight and Maneuverability." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1874–88. http://dx.doi.org/10.22214/ijraset.2022.41671.
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Abstract: Unmanned Aerial Vehicles or UAVs are an amazing piece of machinery that have proven their usefulness in various industries like construction, military, logistics, filmmaking, etc. They’re either operated remotely by a human controller from the ground or have automated capabilities to control its own flight. But due to several technological and environmental restrictions there are many models that can guarantee complete autonomous behavior of the drone. For being truly autonomous, UAVs will need to get far better at sensing its surroundings and obstacles to decide its own path and react in time to avoid collisions. This project specifically aims at enhancing the drones used in the tourism sector for the betterment of traveling experience of people by automating the process of capturing their experience along with autonomous flight capabilities. ML enabled cinematography drones to tackle the gap in the market where good camera drones are unavailable at a lower price point with an added benefit of hands-free automated flight patterns to capture cinematic shots. We have tried to achieve this milestone by using sensors that can detect the surrounding obstacles in real-time and manipulate its flight path to capture cinematic shots of the target object. Through the project we’re also automating various shots like hover, 360 degree view, follow at focus, etc that the drone will include into its flight path on the go. Operating an unmanned flying machine is very challenging especially when you’re flying the drone out of your line of sight and detecting objects using just one FPV camera or monitoring several cameras at the same time is not humanly possible. Hence, through this project we’re not only allowing travellers to enjoy their journey but also take videos of places they can’t reach without having to worry about losing or crashing their drones. There can be further research on this flying model to inculcate it for various industries as the flying pattern and requirement changes in each sector. Keywords: UAV (Unmanned Aerial Vehicle), Drone, Cinematography, ML, FPV, Automated flight
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Jamil, Sonain, Muhammad Sohail Abbas, and Arunabha M. Roy. "Distinguishing Malicious Drones Using Vision Transformer." AI 3, no. 2 (March 31, 2022): 260–73. http://dx.doi.org/10.3390/ai3020016.
Full textAbstract:
Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads. However, malicious drones that carry harmful objects are often adversely used to intrude restricted areas and attack critical public places. Thus, the timely detection of malicious drones can prevent potential harm. This article proposes a vision transformer (ViT) based framework to distinguish between drones and malicious drones. In the proposed ViT based model, drone images are split into fixed-size patches; then, linearly embeddings and position embeddings are applied, and the resulting sequence of vectors is finally fed to a standard ViT encoder. During classification, an additional learnable classification token associated to the sequence is used. The proposed framework is compared with several handcrafted and deep convolutional neural networks (D-CNN), which reveal that the proposed model has achieved an accuracy of 98.3%, outperforming various handcrafted and D-CNNs models. Additionally, the superiority of the proposed model is illustrated by comparing it with the existing state-of-the-art drone-detection methods.